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Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility
Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted g...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499061/ https://www.ncbi.nlm.nih.gov/pubmed/22637743 http://dx.doi.org/10.1093/carcin/bgs188 |
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author | Wei, Sheng Wang, Li-E McHugh, Michelle K. Han, Younghun Xiong, Momiao Amos, Christopher I. Spitz, Margaret R. Wei, Qingyi Wei |
author_facet | Wei, Sheng Wang, Li-E McHugh, Michelle K. Han, Younghun Xiong, Momiao Amos, Christopher I. Spitz, Margaret R. Wei, Qingyi Wei |
author_sort | Wei, Sheng |
collection | PubMed |
description | Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P -values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P- value for the most significant SNP rs13383928 was 2.17×10 (–6) , which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54 , located on 7q32.1 ( P = 8.90×10 (–5) ). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk. Abbreviations: CI: confidence interval E: environment FDR: false discovery rate G: gene GSEA: gene-set-enrichment analysis GWAS: genome-wide association studies i-GSEA: improved gene-set-enrichment analysis approach OR: odds ratio SNP: single nucleotide polymorphism |
format | Online Article Text |
id | pubmed-3499061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-34990612013-08-01 Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility Wei, Sheng Wang, Li-E McHugh, Michelle K. Han, Younghun Xiong, Momiao Amos, Christopher I. Spitz, Margaret R. Wei, Qingyi Wei Carcinogenesis Original Manuscript Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P -values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P- value for the most significant SNP rs13383928 was 2.17×10 (–6) , which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54 , located on 7q32.1 ( P = 8.90×10 (–5) ). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk. Abbreviations: CI: confidence interval E: environment FDR: false discovery rate G: gene GSEA: gene-set-enrichment analysis GWAS: genome-wide association studies i-GSEA: improved gene-set-enrichment analysis approach OR: odds ratio SNP: single nucleotide polymorphism Oxford University Press 2012-08 2012-07-09 /pmc/articles/PMC3499061/ /pubmed/22637743 http://dx.doi.org/10.1093/carcin/bgs188 Text en © The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | Original Manuscript Wei, Sheng Wang, Li-E McHugh, Michelle K. Han, Younghun Xiong, Momiao Amos, Christopher I. Spitz, Margaret R. Wei, Qingyi Wei Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
title | Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
title_full | Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
title_fullStr | Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
title_full_unstemmed | Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
title_short | Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
title_sort | genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility |
topic | Original Manuscript |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499061/ https://www.ncbi.nlm.nih.gov/pubmed/22637743 http://dx.doi.org/10.1093/carcin/bgs188 |
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